Multilevel models Improve precision and Speed of IC 50 Estimates
Open accessAbstract
AIM: Experimental variation in dose-response data of drugs tested on cell lines result in inaccuracies in the estimate of a key drug sensitivity characteristic: the IC50. We aim to improve the precision of the half-limiting dose (IC50) estimates by simultaneously employing all dose-responses across all cell lines and drugs, rather than using a single drug-cell line response. MATERIALS & METHODS: We propose a multilevel mixed effects model that takes advantage of all available dose-response data. RESULTS: The new estimates are highly concordant with the currently used Bayesian model when the data are well behaved. Otherwise, the multilevel model is clearly superior. CONCLUSION: The multilevel model yields a significant reduction of extreme IC50 estimates, an increase in precision and it runs orders of magnitude faster.
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Fitting models to all dose-response series leads to improved robustness for more accurate IC50 and AUC estimates.
“Fitting models to all dose–response series leads to improved robustness for more accurate IC50 and AUC estimates.”
From Exploiting convergent phenotypes to derive a pan-cancer cisplatin response gene expression signature (2023) · cites this paper as [38]
The Introduction and Discussion say that fitting dose-response curves together borrows information across the screen, reduces extreme estimates, and improves the accuracy of IC50 estimates. The Results section (“Concordance and precision”; page not available) reports that AUC estimates correlate well with those from a Bayesian model, but does not establish that this approach makes AUC estimates more accurate. (LLM, conf=0.84)
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“From the raw data, fold-change viability values were calculated using the R package gdscIC50 [43], and standard four-parameter monotonic log-logistic dose-response curves were fitted using the R package dr4pl [44].”
The IC-50-time evolution is a new model to improve drug responses consistency of large scale studies
Alileche, A. (2022) · F1000Research · doi:10.12688/f1000research.108673.1 · Cited by 1 · see citing works
Wang, D. Y. Q., Hensman, J., Kutkaite, G., Toh, T. S., GDSC Screening Team, Dry, J. R., Sáez-Rodríguez, J., Garnett, M. J., Menden, M. P., & Dondelinger, F. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.01.072983 · Cited by 1 · see citing works
Self-citationDruID: Personalized Dru g Recommendations by I ntegrating Multiple Biomedical D atabases for CancerPreprint
Liany, H., Jeyasekharan, A. D. D., & Rajan, V. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.11.439315 · Cited by 1 · see citing works
Exploiting convergent evolution to derive a pan-cancer cisplatin response gene expression signaturePreprint
Scarborough, J. A., Eschrich, S. A., Torres–Roca, J. F., Dhawan, A., & Scott, J. G. (2021) · medRxiv · doi:10.1101/2021.11.10.21265799 · Cited by 1 · see citing works
“Additional information regarding dose-response curve fitting may be found at Vis et al. 28 .”
BackgroundInterpretable neural networks prioritize cancer driver genes from genome-wide dependency landscapesPreprint
Yin, Q., & Chen, L. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.28.651122 · Cited by 1 · see citing works
Mistry, H. B., & Chapman, P. (2018) · doi:10.7287/peerj.preprints.27345v1
Mistry, H. B., & Chapman, P. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/174862
“The next method involved fitting the dose-response model described in (E2) within a hierarchical modelling framework (21) using the nlme R package.”
BackgroundMethodInfluentialKoh, E.-K. (2024) · The Journal of Korean Academy of Physical Therapy Science · doi:10.26862/jkpts.2024.09.31.3.66
Teggar, H., Ahmed, M. M., Nasr, F. A., Noman, O. M., Alqahtani, A. S., Eid, F., Almarfadi, O. M., & Shahat, A. A. (2026) · South African Journal of Botany · doi:10.1016/j.sajb.2026.07.004
- Authors
- Vis, D. J., Bombardelli, L., Lightfoot, H., Iorio, F., Garnett, M. J., & Wessels, L. F.
- Affiliations
- Center for Personalized Cancer Treatment (CPCT), Utrecht/Amsterdam, The Netherlands
- The Netherlands Cancer Institute (NL)
- Wellcome Sanger Institute (GB)
- European Bioinformatics Institute (GB)
- Venue
- Pharmacogenomics
- Publisher
- Informa UK Limited
- Published
- 2016-05
- Volume
- 17
- Issue
- 7
- Pages
- 691-700
- ISSN
- 1462-2416 (ISSN-L)
- 1744-8042
- PMC
- PMC6455999
- OpenAlex
- W2395185160
- Cited by
- 71
- References
- 8
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 8 · 2023: 7 · 2022: 11 · 2021: 7 · 2020: 10 · 2019: 4 · 2018: 5 · 2017: 4 · 2016: 2
- FWCI
- 4.60
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Pharmacogenomics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- IC50 estimation, dose-response data, multilevel mixed-effects models, drug sensitivity, cell lines, computational speed, Bayesian model selection, anticancer drug screening
- MeSH terms
- Pharmacogenomic Testing, Algorithms, Bayes Theorem, Dose-Response Relationship, Drug, Drug Screening Assays, Antitumor, Humans, Models, Biological, Nonlinear Dynamics, Drug Resistance, Neoplasm, Inhibitory Concentration 50, Cell Line, Tumor, Precision Medicine
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